While I value the depth in which the blog authors went into, some of these are not problems with pgvector but problems with similarity search itself and would be problems with other vector databases as well. There are also solutions to the problems describe that the authors do not mention that I think any reader should take into account.

> 1. Required and Negated Words

This is not a bug of pgvector but a bug in embedding models and simple similarity search itself. You'd run into this issue on when doing RAG with any vectordb or ANN search library. You could probably solve this with query expansion, running multiple similarity searches in parallel, and doing filtering of results containing the required theme vs trying to get this all in a single search.

> 2. Explainability With Highlights

This is a misunderstanding of the purpose of embeddings based semantic search. The point of semantic search is not to match on exact keywords but to match on the /meaning/. If you want exact keyword matching, use full text search. This is something that can also be solved by hybrid search, combining full text search and semantic search and using a re-ranker. PostgreSQL has built in FTS with tsvector.

> 3. Performant Filters and Order By’s

The authors do not disclose any details about what index they use here or what kind of filtering they are trying to do. As other commenters point out, the StreamingDiskANN in the pgvectorscale extension [0] (complement to pgvector, you can use them together) improves on performance and accuracy of filtering vs pgvector HNSW (see details in [1]).

>4. Support for sparse vectors, BM25, and other inverse document frequency search modes

This is probably the most fair point in the post. But there exists projects like pg_search from ParadeDB which bring bm_25 to Postgres and help solve this [2]

Lastly, I respect companies trying to provide real-world examples of the trade-offs of different systems, and so thank the authors for sharing their experience and spurring discussion.

Disclaimer: I work at Timescale, where we offer pgvector, and also made other extensions for AI/ vector workloads on PostgreSQL, namely pgvectorscale, and pgai. I've tried to be as even in my analysis as possible but as with everything on the internet, you can make up your own mind and decide for yourself.

[0]: https://github.com/timescale/pgvectorscale/ [1]: https://www.timescale.com/blog/how-we-made-postgresql-as-fas... [2] https://github.com/paradedb/paradedb